Name
Abstract | ||
|---|---|---|
Sepsis is a life-threatening condition caused by an inappropriate immune response to infection, and is a leading cause of elderly death globally. Early recognition of patients and timely antibiotic therapy based on guidelines improve survival rate. Unfortunately, for those patients, it is often detected late because it is too expensive and impractical to perform frequent monitoring for all the elderly. In this paper, we present a risk driven sepsis screening and monitoring framework to shorten the time of onset detection without frequent monitoring of all the elderly. Within this framework, the sepsis ultimate risk of onset probability and mortality is calculated based on a novel temporal probabilistic model named Auto-BN, which consists of time dependent state, state dependent property, and state dependent inference structures. Then, different stages of a patient are encoded into different states, monitoring frequency is encoded into the state dependent property, and screening content is encoded into different state dependent inference structures. In this way, the screening and monitoring frequency and content can be automatically adjusted when encoding the sepsis ultimate risk into the guard of state transition. This allows for flexible manipulation of the tradeoff between screening accuracy and frequency. We evaluate its effectiveness through empirical study, and incorporate it into existing medical guidance system to improve medical healthcare. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/s10916-016-0444-2 | J. Medical Systems |
Keywords | Field | DocType |
Sepsis management, Early detection, Intensive monitoring, Bayesian network, Automata | Early detection,State dependent,Survival rate,Inference,Risk assessment,Intensive care medicine,Bayesian network,Sepsis,Medicine,Bayes' theorem | Journal |
Volume | Issue | ISSN |
40 | 4 | 1573-689X |
Citations | PageRank | References |
2 | 0.41 | 3 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yu Jiang | 1 | 2 | 0.41 |
| L. Sha | 2 | 7376 | 1006.47 |
| Maryam Rahmaniheris | 3 | 19 | 3.97 |
| Binhua Wan | 4 | 2 | 0.74 |
| Mohammad Hosseini | 5 | 27 | 7.12 |
| Pengliu Tan | 6 | 5 | 0.83 |
| Richard B. Berlin Jr. | 7 | 44 | 6.41 |